The ACL anthology network corpus

By Dragomir R. Radev, Pradeep Muthukrishnan, Vahed Qazvinian, Amjad Abu-Jbara. Language Resources and Evaluation, 2009

405 citations recorded by the source corpus.

We introduce the ACL Anthology Network (AAN), a comprehensive manually curated networked database of citations, collaborations, and summaries in the field of Computational Linguistics. We also present a number of statistics about the network including the most cited authors, the most central collaborators, as well as network statistics about the paper citation, author citation, and author collaboration networks.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/e01eae8dea6fbaa1ae7fc83535053932268df430

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Long Range Arena: A Benchmark for Efficient Transformers (2020, methodology): “We use the ACL Anthology Network (AAN; Radev et al., 2013) dataset, which identiﬁes if two papers have a citation link, a common setup used in long-form document matching (Jiang et al., 2019; Yang et al., 2020).”
The ACL Anthology Network Corpus as a Resource for NLP-based Bibliometrics (2013, neutral): “It was started in 2007 by our group (CLAIR) (Radev et al., 2009) at the University of Michigan.”
Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Document Matching (2020, methodology): “We use the ACL Anthology Networks (AAN) Corpus [23] processed by Jiang et. al.”
Nyströmformer: A Nystöm-based Algorithm for Approximating Self-Attention (2021, methodology): “2011), byte-level document retrieval (Radev et al. 2013), image classification on sequences of pixels (Krizhevsky, Hinton et al.”
Cooperative Generator-Discriminator Networks for Abstractive Summarization with Narrative Flow (2019, methodology): “We train two discriminators: one is trained on AAN for decoding both SASS CS and AAN, while the other discriminator is trained on SASS Bio and used exclusively for decoding that subset.”
Relative Positional Encoding for Transformers with Linear Complexity (2021, neutral): “…2018); • Text : movie review sentiment analysis on the IMDB corpus (Maas et al., 2011); • Retrieval : article similarity classiﬁcation on the All About NLP (AAN) corpus (Radev et al., 2013); • Image : object recognition on the CIFAR10 dataset (Krizhevsky, 2009) represented as pixel sequences.”
S2ORC: The Semantic Scholar Open Research Corpus (2020, methodology): “We focus on bibliometrically-enhanced derivations of these corpora, such as the ACL Anthology Network (AAN) (Radev et al., 2009) 6 derived from the ACL Anthology, RefSeer (Huang et al., 2015) derived from CiteSeerX, and Saier and F¨arber (2019) derived from arXiv, which combine useful aspects of…”
Semantic Text Matching for Long-Form Documents (2019, methodology): “Based on this assumption, the Jaccard similarity [22] between the outgoing links [37] for the experiments.”
GORC: A large contextual citation graph of academic papers (2019, background): “The ACL Anthology Network (AAN) 3 (Radev et al., 2009) is one such contextual citation graph built from the ACL Anthology corpus (Bird et al., 2008), consisting of 24.6K papers manually augmented with citation information.”
Simplified State Space Layers for Sequence Modeling (2022, background): “[46] Dragomir R Radev, Pradeep Muthukrishnan, and Vahed Qazvinian.”
Multi-document Summarization via Deep Learning Techniques: A Survey (2020, methodology): “In the SciSumm dataset, the 1,000 most cited papers from the ACL Anthology Network [128] are treated as reference papers, and an average 15 citation sentences are provided after cleaning.”
PaperRobot: Incremental Draft Generation of Scientific Ideas (2019, methodology): “We re-build the system based on 23,594 NLP papers from the new ACL Anthology Network (Radev et al., 2013).”
cosFormer: Rethinking Softmax in Attention (2022, methodology): “We evaluate our method on a variety of tasks including Long sequence ListOps (Nangia & Bowman, 2018), Byte-level text clas-siﬁcation (Maas et al., 2011), document retrieval using the ACL Anthology Network (Radev et al., 2013), image classiﬁcation on sequence of pixels on CIFAR-10 (Krizhevsky & Hinto”
Long-Short Transformer: Efficient Transformers for Language and Vision (2021, methodology): “This task is based on the ACL Anthology Network dataset [49].”